Bayesian Annotation Networks for Complex Sequence Analysis

Henning Christiansen, Christian Theil Have, Ole Torp Lassen & Matthieu Petit
Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistication and generality, at the cost of potentially very high computational complexity. A methodology is proposed for modularization of such models into sub-models, each representing a particular interpretation of the input data to be analysed. Their composition forms, in a natural way, a Bayesian network, and we...
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